Hansson, Oskar H.

Abstract Background Ten years after the original publication, the Centiloid framework is now broadly used to harmonize amyloid‐PET quantification, facilitate data sharing and comparison across cohorts, and even assist visual interpretation in clinical settings. We evaluated the global implementation of Centiloids by comparing their distribution and corresponding positivity thresholds across cohorts. Methods We gathered data from publicly available cohorts and reached out to investigators across the world to collect cross‐sectional Centiloids, demographic and clinical information, and visual read data. Gaussian mixture models (GMM, k=2) were fitted to Centiloid values for each cohort and cutoffs were calculated as mean + 2SD of the lower Gaussian. When visual reads were available, we determined Centiloid cutoffs that maximized correspondence with visual reads (Cohen's kappa). Data was combined across cohorts using random effects meta‐analyses. Results As of January 2025, we included 37 cohorts ( n = 41,678 participants) with heterogeneous pipelines, radiotracers, and clinical and demographic characteristics (Table‐1). The low Gaussian peaks ranged from ‐9 to 10CL; the meta‐analysis identified a common peak at 1CL. The second peaks were more heterogeneous (range=38‐102CL; meta‐analysis outcome=64CL). Across cohorts, the proportion of cognitively unimpaired versus impaired participants impacted the position of both peaks, with better separation in cohorts enriched in impaired individuals (Figure 1C). The meta‐analysis indicated a GMM‐based cutoff of 19CL (95%CI: 16‐21CL, Figure 1B); subgroup analyses showed no evidence of significant effect between single versus multicenter settings (17 versus 20CL, p = 0.30), MRI‐based or PET‐only processing (18 versus 19CL, p = 0.88), and no evidence of difference across radiotracers (Flutemetamol: 16CL; PIB: 17CL, Flutafuranol: 18CL, Florbetaben: 19CL, Florbetapir: 20CL, p = 0.78). In a subset of 29,496 participants with visual reads available, binary visual reads corresponded well to Centiloids (common kappa=0.86, Figure 2A). The visual read‐based cutoff of 24CL (95%CI: 21‐27CL, Figure 2B) maximized correspondence between visual read and quantification and was slightly higher than the GMM‐based cutoff. All meta‐analysis models showed high non‐random heterogeneity (I 2 >80%) across studies, suggesting non‐random differences in peaks and cutoffs. Conclusions Meta‐analysis‐based cutoffs align well with thresholds from the existing literature. High heterogeneity among studies underscores the need to investigate contributing factors, raising concerns about applying common cutoffs.

Abstract Background Sex, education and race/ethnicity are all associated with risk of Alzheimer's disease dementia. Here, we assess the effects of self‐reported sex, educational attainment and race/ethnicity on amyloid‐positivity, and tau‐PET‐positivity in 12,048 (7,394 cognitively unimpaired [CU], 2,177 MCI, and 2,477 dementia) individuals from 42 cohorts worldwide. Method Logistic generalized estimating equations were used to estimate frequency of amyloid‐positivity (using cohort‐specific thresholds for amyloid‐PET [84%] or CSF) and tau‐PET‐positivity (cohort‐specific thresholds of 2SD above mean temporal uptake in amyloid‐negative controls). We assessed: i) sex and APOEε4 ( N = 10,098) associations, to complement earlier findings of a higher frequency of tau‐positivity in females, ii) effects of lower/higher education ( N = 10,970; cohort‐specific median‐split), and iii) effects of race/ethnicity (non‐Hispanic White [hereafter: White], N = 4880; Asian, N = 116; Black or African‐American [hereafter: Black], N = 353; Hispanic, N = 356, only from Northern‐American cohorts). Outcomes were frequency of amyloid‐positivity in CU individuals only, and tau‐PET‐positivity in both amyloid‐positive (AB+) CU and cognitively impaired (CI, i.e. MCI and dementia) individuals. Interaction effects on the relationship between age and amyloid/tau‐positivity were assessed and only retained in the models when significant. Result Female sex was associated with an APOEε4 ‐independent increased frequency of amyloid‐positivity (β=0.51[0.22], p = 0.02) in CU and increase of tau‐positivity in both AB+CU (β=0.27[0.08]) and AB+CI (β=0.37[0.08], both p <0.01). Remarkably, tau‐positivity frequencies of female APOEε4 non‐carriers were equivalent to male APOEε4 carriers in AB+CI (Figure 1). No significant sex* APOE interactions were observed. In CU, higher education was associated with lower amyloid‐positivity frequency (β=‐0.12[0.05], p = 0.02). In contrast, among AB+CU, there was an age*education interaction effect that indicated more pronounced age effects on tau‐positivity in individuals with higher education (age*education:β interaction =0.03[0.01], p <0.01). There were no education effects in AB+CI (Figure 2). In CU, an age*race/ethnicity interaction effect was observed across all non‐White groups compared to White (Hispanic:β interaction =‐0.05[0.01], p <0.01; Black:‐0.04[0.01], p <0.01; Asian:‐0.02[0.01], p = 0.04). This suggests that the impact of age on amyloid‐positivity was less pronounced in non‐White groups. Furthermore, in AB+CI, Hispanic ethnicity was related to higher tau‐positivity frequency than White (β=0.51[0.22], p = 0.02; Figure 3). Conclusion In this multi‐center initiative comprised of clinical and community‐based cohorts, we observed that self‐reported sex, educational attainment and race/ethnicity were related to positivity‐frequencies of Alzheimer's disease pathology.

Abstract Background The Joint Propagation Model (JPM)‐based CenTauR scale was recently introduced to harmonize tau‐PET quantification across different radiotracers. This study examined how CenTauR harmonization enhances the comparability of tau‐PET quantification across matched cohorts using different tracers. The evaluation focused on three key aspects: (1) comparing tau‐PET positivity rates as defined by CenTauR, (2) evaluating its diagnostic accuracy for symptomatic AD, and (3) analyzing longitudinal changes in tau‐PET rates over time. Method The JPM, developed by the CPAD‐led Tau PET Harmonization Working Group (Leuzy et al., Alzheimers Dement. 2024; Figure 1A), models relationships between anchor point subjects and head‐to‐head tau‐PET SUVR data onto the CenTauR scale, providing conversion equations for multiple tracers. JPM equations were applied to [ 18 F]flortaucipir SUVR (Meta‐temporal ROI) data from 561 cognitively impaired participants in the ADNI and OASIS‐3 studies. Using ROC analysis, we determined the CenTauR cut‐off for positivity (T+) that maximized the Youden index for discriminating between FDA‐approved positive/negative visual reads. Three separate cohorts, scanned using [ 18 F]flortaucipir (ADNI), [ 18 F]MK‐6240 (CPAS), and [ 18 F]RO‐948 (BioFINDER‐2), were matched 1:1 by age, MMSE, amyloid‐β, and clinical diagnosis. CenTauR‐based metrics were compared across these cohorts. Result A cut‐off of 18 CenTauRs was found to optimally classify [ 18 F]flortaucipir PET visual reads (Figure 1B). The matching procedure identified 1089 participants (363 per cohort). The frequency of T+ using the 18 CenTauRs cut‐off for the Meta‐Temporal ROI was highly comparable across tracers across groups, except for the Aβ‐positive cognitively unimpaired group, where variability was more pronounced due to a smaller sample size (Figure 2A). Similarly, tau‐PET discriminative accuracy for symptomatic AD vs controls remained similar across [ 18 F]flortaucipir and [ 18 F]MK‐6240 (Figure 2B). In a separate sample of 212 matched participants from ADNI ([ 18 F]flortaucipir) and AIBL ([ 18 F]MK‐6240) with baseline and 1‐year follow‐up tau‐PET scans, 1‐year change in CenTauRs was comparable (Figure 2C). An exploratory voxelwise transformation, utilizing the Meta‐Temporal conversion equation in a representative case scanned with both [ 18 F]flortaucipir and [ 18 F]MK‐6240, demonstrates the potential for voxelwise CenTauR harmonization (Figure 3). Conclusion These analyses suggest that CenTauR harmonization increases the comparability of tau‐PET data acquired with different tracers. Additional validation analyses with larger cohorts including other radiotracers ([ 18 F]PI‐2620) are underway.

Abstract Background The link between regional tau load and clinical manifestation of Alzheimer's disease (AD) highlights the importance of characterizing spatial tau distribution. In typical (memory‐predominant) AD, the spatial progression of tau pathology mirrors the functional connections from temporal lobe epicenters. However, atypical (non‐amnestic‐predominant) AD variants with heterogeneous tau patterns provide a key opportunity to assess the universality of connectivity as a scaffold for tau progression. Method We included tau‐PET data from 320 subjects with atypical AD, characterized by highly heterogeneous tau patterns ( n = 139 posterior cortical atrophy/PCA‐AD; n = 103 logopenic variant primary progressive aphasia/lvPPA‐AD; n = 35 behavioural variant AD/bvAD; n = 43 corticobasal syndrome/CBS‐AD) from 14 sites, with a subset of patients ( n = 78) having longitudinal tau‐PET data. As an independent sample, we further included regional post‐mortem tau stainings from 93 atypical AD patients from two sites ( n = 19 PCA‐AD, n = 32 lvPPA‐AD, n = 23 bvAD, n = 19 CBS‐AD). Gaussian mixture modeling was used to harmonize different tau‐PET tracers by transforming tau‐PET standardized uptake value ratios to tau positivity probabilities (a uniform scale ranging from 0% to 100%). Using linear regression, we assessed whether 1) brain regions with stronger functional connectivity showed greater covariance in cross‐sectional and longitudinal tau‐PET and post‐mortem tau pathology, and 2) functional connectivity of tau‐PET epicenters and tau‐PET accumulation epicenters was associated with cross‐sectional and longitudinal tau patterns. Result Tau‐PET epicenters—defined as the 5% brain regions with the highest tau load—aligned with clinical variants, e.g. a posterior pattern in PCA‐AD (“visual AD”) and left‐hemispheric temporal predominance in lvPPA‐AD (“language AD”) (Figure 1). More strongly functionally connected regions showed correlated concurrent tau‐PET levels, which was confirmed with post‐mortem data (Figure 2). Moreover, the connectivity profile of tau‐PET epicenters and accumulation epicenters corresponded to tau‐PET progression patterns (Figure 3). Conclusion Our data are consistent with the hypothesis that tau propagation occurs along functional connections originating from local epicenters, across all AD clinical variants. Since tau proteinopathy is a key driver of neurodegeneration and cognitive decline, this finding may advance personalized medicine and participant‐specific endpoints in clinical trials.

Abstract Background Cerebrospinal fluid (CSF) seed amplification assays (SAAs) for detecting α‐synuclein (αsyn) seeds have recently emerged as robust in vivo biomarkers of Lewy body pathology (LBP). However, CSF sampling is invasive, costly, and time‐consuming, limiting its widespread use. We aimed to develop and evaluate a neuropathologically‐validated two‐step workflow, leveraging smell function and CSF αsyn SAA to accurately determine postmortem LBP status. This approach could reduce the number of confirmatory lumbar punctures needed (Figure 1). Methods The study included 358 individuals from the Arizona Study of Aging and Neurodegenerative Disorders with antemortem smell testing, CSF αsyn SAA results, and postmortem neuropathological assessments of regional LBP burden. Step‐1 of the two‐step workflow involved a risk‐stratification model predicting postmortem cortical LBP‐positivity (LBP ctx , defined as at least mild pathology in any cortical region) using logistic regression models with University of Pennsylvania Smell Identification Test (UPSIT) scores (smell testing), age, and sex as predictors. In step‐2, confirmatory CSF αsyn SAA testing was applied only to participants identified as high‐risk in step‐1. Workflow performance ‐ accuracy, positive predictive value (PPV), negative predictive value (NPV), and reduction in CSF testing ‐ was evaluated in (i) the entire study cohort; (ii) patients with clinical parkinsonism; (iii) patients with an Alzheimer's disease (AD) clinical syndrome; and (iv) clinically unimpaired (CU) individuals. Results Participants had a mean age at death of 86.2±7.8 years, 42.6% were female, 35.2% had LBP ctx , and the average time between UPSIT and death was 3.2±2.3 years (Table 1). Using a 95% sensitivity cut‐off for the UPSIT‐based algorithm, the two‐step workflow achieved high accuracy in identifying LBP ctx (whole cohort=94%; clinical parkinsonism=95%; clinical AD=94%; CU=93%; Figure 2a), while reducing CSF testing (whole cohort=‐43%; clinical parkinsonism=‐23%; clinical AD=‐35%; and CU=‐80%; Figure 2d). PPVs ranged from 75‐96% and were highest in the clinical parkinsonism subgroup (96%) where LBP ctx + was highest (62.7%). NPVs ranged between 95‐98% and were highest in the clinical AD subgroup (98%) (Figure 2b‐c). The two‐step approach reached accuracies similar to using CSF tests in all participants. Conclusions Implementing a two‐step workflow in different clinical scenarios may reduce invasive testing with CSF, minimizing the burden for individuals and costs for healthcare providers.

Abstract Background The objective of this study was to determine the predictive value of amyloid‐PET versus plasma %p‐tau217 (defined as the ratio of phosphorylated to non‐phosphorylated Tau217 × 100), Aβ42/40, and the amyloid probability score (APS2; which combines of Aβ42/40 and %p‐tau217) in tau‐PET transitions (T‐ to T+). Method Participants included were from the Mayo Clinic Study of Aging (MCSA) and had plasma markers measured by mass spectrometry (IP‐MS; C2N), had an amyloid‐PET scan, and had a tau‐PET negative scan (SUVR<1.29, temporal meta‐ROI) at the baseline, along with one or more follow‐up tau‐PET scans. Cox proportional hazard models were used to evaluate the relationship between C2N plasma %P‐Tau217 and APS2 and incident tau‐PET positivity (SUVR>=1.29) adjusting for age, sex, and APOE ε4. An analysis was also performed using amyloid‐PET centiloid as the predictor. Both the plasma markers and amyloid‐PET centiloid were scaled so that a one‐unit change corresponds to the respective interquartile range (IQR) for comparability of hazard ratios (HR). The analyses were repeated in the BioFINDER‐2 cohort for validation. Result 255 MCSA participants (122 female [47.8%] were included with a median age of 71.9 (IQR: 65.8, 78.8) years old, of which 37 progressed to tau‐PET positivity. At baseline, 238 (93.7%) were cognitively unimpaired. Higher baseline %p‐tau217 (HR: 1.52 [95% CI: 1.28, 1.8]), amyloid‐PET centiloid (HR: 1.47 [95% CI: 1.20, 1.79]), and APS2 (HR: 1.62 [95% CI: 1.22, 2.16]) were associated with an increased risk of progressing to tau‐PET positive (Figure 1). Aβ42/40 (HR: 1.06 [95% 0.604–1.867] was not associated with incident tau positivity Among 605 participants who were tau negative at index date in BioFINDER‐2 (316 (52.2%) cognitively unimpaired), with a median age of 70.2 (IQR: 60.5‐76.4), 33 progressed to tau positivity with higher %p‐tau217 (HR: 1.8 [95% CI: 1.5, 2.17]), amyloid‐PET centiloid (HR: 2.3 [95% CI: 1.77, 2.97]), and lower Aβ 42/40 (HR: 0.42 [95% CI: 0.21, 0.85]) at index date all associated with an increased risk of progressing to tau‐PET positive. Conclusion In two independent cohorts , %p‐tau217 provides predictive information about those who will convert from tau negative to tau positive on PET scan with the associations being similar to amyloid‐PET.

Abstract Background In recent years, blood‐based biomarkers (BBMs) have transformed the diagnostic landscape of Alzheimer’s disease (AD), with some now approaching readiness for clinical implementation. This progress aligns with the growing importance of accurate early diagnostics and availability of anti‐Aβ therapies for the treatment of early symptomatic AD, reinforcing the need for more rapid and early diagnostic capabilities. To address this need, the Alzheimer’s Association convened a multidisciplinary panel of clinical experts, subject‐matter specialists, and guideline methodologists to conduct a systematic review and develop evidence‐based recommendations for the use of BBMs in the diagnostic evaluation of AD. The scope of this guideline is focused on individuals with cognitive impairment ‐ either MCI or dementia ‐ who are undergoing diagnostic assessment in secondary or tertiary care settings. Method The panel conducted a systematic review to assess BBMs' diagnostic test accuracy in detecting amyloid pathology for triaging (≥90% sensitivity, ≥75% specificity) and confirmatory (≥90% sensitivity and specificity) diagnostic workup. The BBMs of interest included plasma p ‐tau and Aβ tests measuring the following analytes: p ‐tau217, % p ‐tau217, p ‐tau181, p ‐tau231, and Aβ42/Aβ40 ratio. The reference standard tests included CSF, amyloid PET, or neuropathology examination. The panel applied the GRADE approach to assess the certainty of the evidence and the GRADE Evidence‐to‐Decision Framework to develop its recommendations. Result Across 49 observational studies meeting eligibility criteria, 31 different BBM tests were evaluated. Using predefined decision thresholds, the panel determined whether each test has 1) sufficient diagnostic test accuracy to be used as a triaging test where a positive test is to be confirmed by PET or CSF, 2) sufficient diagnostic test accuracy as a confirmatory test to replace PET or CSF, or 3) insufficient diagnostic test accuracy to recommend current use in clinical practice. Recommendations will be provided in case any BBMs met a priori DTA thresholds. Conclusion BBMs can improve early AD diagnosis and expand access to disease‐modifying therapies. Evidence‐based guidelines are key to standardizing their use and will be updated as new evidence and applications emerge.

Abstract Background Ten years after the original publication, the Centiloid framework is now broadly used to harmonize amyloid‐PET quantification, facilitate data sharing and comparison across cohorts, and even assist visual interpretation in clinical settings. We evaluated the global implementation of Centiloids by comparing their distribution and corresponding positivity thresholds across cohorts. Methods We gathered data from publicly available cohorts and reached out to investigators across the world to collect cross‐sectional Centiloids, demographic and clinical information, and visual read data. Gaussian mixture models (GMM, k=2) were fitted to Centiloid values for each cohort and cutoffs were calculated as mean + 2SD of the lower Gaussian. When visual reads were available, we determined Centiloid cutoffs that maximized correspondence with visual reads (Cohen’s kappa). Data was combined across cohorts using random effects meta‐analyses. Results As of January 2025, we included 37 cohorts ( n = 41,678 participants) with heterogeneous pipelines, radiotracers, and clinical and demographic characteristics (Table‐1). The low Gaussian peaks ranged from ‐9 to 10CL; the meta‐analysis identified a common peak at 1CL. The second peaks were more heterogeneous (range=38‐102CL; meta‐analysis outcome=64CL). Across cohorts, the proportion of cognitively unimpaired versus impaired participants impacted the position of both peaks, with better separation in cohorts enriched in impaired individuals (Figure‐1C). The meta‐analysis indicated a GMM‐based cutoff of 19CL (95%CI: 16‐21CL, Figure‐1B); subgroup analyses showed no evidence of significant effect between single versus multicenter settings (17 versus 20CL, p = 0.30), MRI‐based or PET‐only processing (18 versus 19CL, p = 0.88), and no evidence of difference across radiotracers (Flutemetamol: 16CL; PIB: 17CL, Flutafuranol: 18CL, Florbetaben: 19CL, Florbetapir: 20CL, p = 0.78). In a subset of 29,496 participants with visual reads available, binary visual reads corresponded well to Centiloids (common kappa=0.86, Figure‐2A). The visual read‐based cutoff of 24CL (95%CI: 21‐27CL, Figure‐2B) maximized correspondence between visual read and quantification and was slightly higher than the GMM‐based cutoff. All meta‐analysis models showed high non‐random heterogeneity (I 2 >80%) across studies, suggesting non‐random differences in peaks and cutoffs. Conclusions Meta‐analysis‐based cutoffs align well with thresholds from the existing literature. High heterogeneity among studies underscores the need to investigate contributing factors, raising concerns about applying common cutoffs.

Abstract Background The Joint Propagation Model (JPM)‐based CenTauR scale was recently introduced to harmonize tau‐PET quantification across different radiotracers. This study examined how CenTauR harmonization enhances the comparability of tau‐PET quantification across matched cohorts using different tracers. The evaluation focused on three key aspects: (1) comparing tau‐PET positivity rates as defined by CenTauR, (2) evaluating its diagnostic accuracy for symptomatic AD, and (3) analyzing longitudinal changes in tau‐PET rates over time. Method The JPM, developed by the CPAD‐led Tau PET Harmonization Working Group (Leuzy et al., Alzheimers Dement. 2024; Figure 1A), models relationships between anchor point subjects and head‐to‐head tau‐PET SUVR data onto the CenTauR scale, providing conversion equations for multiple tracers. JPM equations were applied to [¹⁸F]flortaucipir SUVR (Meta‐temporal ROI) data from 561 cognitively impaired participants in the ADNI and OASIS‐3 studies. Using ROC analysis, we determined the CenTauR cut‐off for positivity (T+) that maximized the Youden index for discriminating between FDA‐approved positive/negative visual reads. Three separate cohorts, scanned using [¹⁸F]flortaucipir (ADNI), [¹⁸F]MK‐6240 (CPAS), and [¹⁸F]RO‐948 (BioFINDER‐2), were matched 1:1 by age, MMSE, amyloid‐β, and clinical diagnosis. CenTauR‐based metrics were compared across these cohorts. Result A cut‐off of 18 CenTauRs was found to optimally classify [ 18 F]flortaucipir PET visual reads (Figure 1B). The matching procedure identified 1089 participants (363 per cohort). The frequency of T+ using the 18 CenTauRs cut‐off for the Meta‐Temporal ROI was highly comparable across tracers across groups, except for the Aβ‐positive cognitively unimpaired group, where variability was more pronounced due to a smaller sample size (Figure 2A). Similarly, tau‐PET discriminative accuracy for symptomatic AD vs controls remained similar across [ 18 F]flortaucipir and [ 18 F]MK‐6240 (Figure 2B). In a separate sample of 212 matched participants from ADNI ([¹⁸F]flortaucipir) and AIBL ([¹⁸F]MK‐6240) with baseline and 1‐year follow‐up tau‐PET scans, 1‐year change in CenTauRs was comparable (Figure 2C). An exploratory voxelwise transformation, utilizing the Meta‐Temporal conversion equation in a representative case scanned with both [¹⁸F]flortaucipir and [¹⁸F]MK‐6240, demonstrates the potential for voxelwise CenTauR harmonization (Figure 3). Conclusion These analyses suggest that CenTauR harmonization increases the comparability of tau‐PET data acquired with different tracers. Additional validation analyses with larger cohorts including other radiotracers ([¹⁸F]PI‐2620) are underway.